Integrated Artificial Intelligence and Quantum Chemistry Approach for the Rational Design of Novel Antibacterial Agents against Ralstonia solanacearum.

Antimicrobial resistance (AMR) in plant pathogenic bacteria poses a serious threat to global agriculture, necessitating the development of novel antibacterial agents targeting virulence mechanisms. This study presents an integrated bioinformatics-driven framework for the rational design and computational validation of Solres, a newly designed small molecule targeting key virulence proteins in phytopathogenic bacteria. Approximately 10,000 active compounds from PubChem BioAssay (AID: 588726) were analyzed using structural clustering and scaffold mining to identify conserved molecular motifs associated with antibacterial activity. Guided by high-frequency substructures, Solres was designed de novo and screened for structural novelty against PubChem, ChEMBL, and WIPO databases. Drug-likeness evaluation using Lipinskis Rule of Five confirmed favorable physicochemical properties. Molecular docking was performed against essential virulence regulators, including PhcA, PhcR, HrpB, PehA, and Egl from Ralstonia solanacearum and Xanthomonas spp., with active sites predicted using CaspFold. Docking analyses revealed strong binding affinities and stable interactions with key catalytic and regulatory residues. Complex stability and conformational integrity were further validated through molecular dynamics simulations. Quantum chemical descriptors, including HOMO-LUMO energy gap and dipole moment, supported the electronic suitability and reactivity profile of Solres. Collectively, this study demonstrates the effective integration of cheminformatics, structural bioinformatics, molecular simulations, and quantum chemical analyses for plant-focused antibacterial discovery. The compound Solres represents a promising lead candidate for mitigating bacterial wilt disease and provides a computational framework for future experimental validation and sustainable crop protection strategies against AMR-driven phytopathogens.